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220 results for “landmarks”
Fig. 10 in Landmark and outline-based geometric morphometrics analysis of three Stomoxys flies (Diptera: Muscidae)
Fig. 10. Outline-based discriminant analysis. Factor map of canonical variates (i.e. discriminant factors) derived from the principal components of the Normalised Elliptic Fourier coefficients of three species of Stomoxys Geoffroy, 1762, in males (A) and females (B).
Fig. 6 in Landmark and outline-based geometric morphometrics analysis of three Stomoxys flies (Diptera: Muscidae)
Fig. 6. Configurations of the ten anatomical landmarks connected by a straight line after procrustes superimposition of three species of Stomoxys Geoffroy, 1762, in males (A) and females (B).
Fig. 2 in Landmark and outline-based geometric morphometrics analysis of three Stomoxys flies (Diptera: Muscidae)
Fig. 2. Morphological characters of tibia and tarsus used to separate Stomoxys pullus Austen, 1909 (A), S. uruma Shinonaga et Kano, 1966 (B) and S. indicus Picard, 1908 (C).
Fig. 3 in Landmark and outline-based geometric morphometrics analysis of three Stomoxys flies (Diptera: Muscidae)
Fig. 3. Ten landmarks digitised on wings of species of Stomoxys Geoffroy, 1762 flies for landmark-based geometric morphometrics analysis (see Table 2 for description).
Fig. 9 in Landmark and outline-based geometric morphometrics analysis of three Stomoxys flies (Diptera: Muscidae)
Fig. 9. Configurations of the outlines after Elliptic Fourier Analysis of Stomoxys pullus Austen, 1909, S. uruma Shinonaga et Kano, 1966 and S. indicus Picard, 1908, in males (A) and females (B). Areas outlined by different colours represent shape, not size.
Fig. 5 in Landmark and outline-based geometric morphometrics analysis of three Stomoxys flies (Diptera: Muscidae)
Fig. 5. Centroid size variation of the wings between species and sexes, shown as quartile boxes. Each box shows the group median separating the 25th and 75th quartiles. Vertical bars under the boxes represent the wing (units as mm).
Fig. 1 in Landmark and outline-based geometric morphometrics analysis of three Stomoxys flies (Diptera: Muscidae)
Fig. 1. Morphological characters of palpi used to separate Stomoxys pullus Austen, 1909 (A), S. uruma Shinonaga et Kano, 1966 (B) and S. indicus Picard, 1908 (C).
Fig. 8 in Landmark and outline-based geometric morphometrics analysis of three Stomoxys flies (Diptera: Muscidae)
Fig. 8. Perimeter variation of the wings between species and sexes, shown as quartile boxes. Each box shows the group median sepa- rating the 25th and 75th quartiles. Vertical bars under the boxes represent the wing (units as mm).
Fig. 7 in Landmark and outline-based geometric morphometrics analysis of three Stomoxys flies (Diptera: Muscidae)
Fig. 7. Landmark-based discriminant analysis. Factor map of canonical variates resulting from comparison among the three species of Stomoxys Geoffroy, 1762, in males (A) and females (B).
Fig. 4 in Landmark and outline-based geometric morphometrics analysis of three Stomoxys flies (Diptera: Muscidae)
Fig. 4. Contour digitised on Stomoxys Geoffroy, 1762 flies wing for outline-based geometric morphometrics analysis. A short, artificial segment is computed by the digitising program to completely close the contour.
Landmark-Aware Visual Navigation (LAVN) Dataset
<p>We present a \textbf{L}andmark-\textbf{A}ware \textbf{V}isual \textbf{N}avigation (LAVN) dataset to allow for supervised learning of human-centric exploration policies. We collect RGB observation and human point-click pairs as a human annotator explores virtual and real world environments with the goal of full coverage exploration of the space. These human point-clicks serve as direct supervision for waypoint prediction when learning to explore in environments. The human annotators also provide distinct landmark examples along each trajectory, which we intuit will simplify the task of map or graph building and localization. </p>
Ecological signal in the size and shape of marine amniote teeth – 3D models and landmarks
Open the record for dataset details and reuse information.
Tsetse fly wing landmark data for morphometrics (Vol 20, 21)
Open the record for dataset details and reuse information.
Armed to the teeth: Supplementary material, R code, Rdata, mesh, landmarks
Open the record for dataset details and reuse information.
SGS-LTER GIS layer with detailed information on physical landmarks on Central Plains Experimental Range, Nunn, Colorado, USA 2012
This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. No Abstract Available
Annotated Facial Landmarks for attention and inattention
<p>This data consists of 34 facial landmark features of 18 children with autism spectrum disorder and 26 typically developing children between ages 7 and 11 years old during attention tasks experiment, The attention task consists of 4 levels where random letters are displayed on the board presented in a virtual classroom: level 1(none: without distractions). Other observation samples were generated from level 2-4 (easy, medium, and hard: with distractions).</p> <p>iMotions software detects to generate the facial landmarks from five regions of the face: eyes, eyebrows, nose, lips, and jaw. Two class labels— Attention and Inattention were generated based on task performance and some attention behavioral rules, such as looking at the target letters.</p> <p>The video stream data generated during the attention task is divided into separate time slots for each random letter displayed. Each letter appears for the duration of 1400ms. Then each slot was labeled as attention and inattention for correct (X-click) and incorrect click (Missed X), respectively. The total observation samples were extracted from 95 videos of all the participants. Each length of the video was 300 seconds long and iMotions software reads the video stream at 16 frame rate per second. The frames were annotated for attention and inattention when the participants responded to the target letter and iMotions generated 16 data samples in1 second. </p>
Anthony-Northside historic 1928 landmark
Anthony-Northside historic stone gateway landmark, built in 1928, at the intersection of Wheeling Avenue and Cowing Drive in Muncie, Indiana. Visit gateway.muncieanna.com. Source: Objaverse 1.0 / Sketchfab
Great Kiva: Lowry Pueblo Nat'l Hist. Landmark
Lowry Pueblo National Historic Landmark in Canyons of the Ancients National Monument is an Ancestral Puebloan surface village constructed and utilized during the late Pueblo II and early Pueblo III periods (AD 1085 to 1240). The core and veneer masonry at the site is predominately flaked-and-pecked blocky sandstone, wet-laid in local clay mortar. Construction characteristics evince some association with the Chaco-style developed in Northern New Mexico, demonstrating at least cursory connections between these two important cultural areas during the middle of the 12th Century. The site consists of 39 masonry surface rooms, seven masonry kivas, a cylindrical masonry tower, and separate masonry Great Kiva. This Great Kiva appears to have been the civic focal point for the surrounding community, which consists of over 40 additional contemporaneous structures within a one square mile area. Source: Objaverse 1.0 / Sketchfab
Figure 1. Landmarks head selection. A. Rhodnius prolixus. B in Head geometric morphometrics of two Chagas disease vectors from Venezuela
Figure 1. Landmarks head selection. A. Rhodnius prolixus. B. Triatoma maculata. Scale bar = 1 mm.
Acute pseudo-landmarking and Constellation homologies: A generalized workflow to identify and track segmented structures in plant time series images
<p>Assessing plant phenotypes throughout the lifecycle is integral to exploring the development, genetics, and evolution of morphology, and can be critical for agronomic and basic research studies. Although various automated or semi-automated phenomic approaches have been developed, it has been challenging to analyze differential growth because of difficulties in segmenting and annotating specific structures or positions in the plant body and maintaining their identities throughout time-series data. To address this gap, we have developed a generalized workflow linking our previously published function, <i>Acute</i>, with a companion homology workflow, <i>Constellation</i>, in the PlantCV environment. <i>Acute</i> identifies acute shapes (pseudo-landmarks) in the plant body, most often corresponding to leaf tips and ligular regions. <i>Constellation</i> uses a strategy of dimensionality reduction via <i>starscape</i> followed by hierarchical clustering through <i>constella </i>to identify 'constellations' of segments in eigenspace that represent the same landmark in consecutive images of a time-series. We devised a quality control function, <i>constellaQC</i>, to test the accuracy of the clustering approach, and use it to show that the approach appropriately clusters the pseudo-landmarks derived from <i>Acute</i>, with 80-90% accuracy. We discuss the reasons for and consequences of this lack of 100% accuracy in automated workflows and suggest how to develop these functions for other phenomics datasets that may vary in dimensional complexity.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.